People learn in many different ways. For example, some people learn best by seeing (i.e., visual learning), while others learn best by hearing (i.e., verbal or auditory learning) or by doing (i.e., kinetic learning). Whereas some people prefer inductive learning (that is, a bottom-up, detail-to-overview or specific-to-general approach), others prefer deductive learning (that is, a top-down, overview-to-detail or general-to-specific approach). Some people prefer to passively absorb knowledge imparted by others, while others prefer to actively discover knowledge on their own. And whereas some people prefer theoretical explanations, others prefer practical examples. The list goes on-numbers, pictures, tasks, repetition, even physical movement-may all affect (positively or negatively) an individual's learning effectiveness.
Thus, different learning strategies have emerged to accommodate the learning preferences of a given person. When designing an educational course, a training author may orient all or part of the material toward, for example, visual, verbal or kinetic learning; inductive or deductive learning; passive or active learning; or theory-based or example-based learning. Moreover, the training author may combine elements of one learning strategy with elements of another learning strategy, for example to accommodate verbal passive learners. Further learning preferences and strategies are also known in the art.
The same principles have been applied to computer-based training (CBT), also known as e-learning, which usually runs in an environment or using an environment called a learning management system or e-learning system. These systems must also support the learning strategies described above. Traditionally, a CBT course comprises a number of sections, wherein each section may target a specific learning strategy or strategies, for example theory-based learning or theory-based, inductive learning. The training author creates metadata for each section that identifies the learning strategy or strategies associated with that section. When the student-user begins the course, he or she selects a preferred learning strategy or strategies. Based on the student-user's selection, the CBT system uses the predefined metadata to automatically generate a static “path” of sections through the training data.
For example, suppose a training author wished to teach five concepts, with each concept being taught using either an example-based or theory-based learning strategy. The training author would create ten sections, one example-based and one theory-based for each of the five concepts. Each section would be described by metadata indicating whether the section were example-based or theory-based. The training author could also optionally create, for example, an overview section through which all student-users would progress, regardless of their preferred learning strategy. Suppose further, for example, that a student-user selected a theory-based learning strategy. At that time, the CBT system would create a path of six sections—the overview section and each of the five theory-based sections—through which that user would progress to complete the training course.
Although the traditional system allows a student-user to choose his or her preferred learning strategy, it may still result in an inappropriate or suboptimal learning strategy for that user. For example, a student-user may not know which strategy best complements his or her learning style. Also, the student-user may not understand how his or her choice affects the individualized presentation of material. For whatever reason, the student-user may select a learning strategy at random. If an inappropriate or suboptimal learning strategy is thus chosen, then the student-user may not learn the material as thoroughly or efficiently as possible.
One alternative to the traditional CBT learning strategy selection method is a variety of standards and specifications which support a feature usually called sequencing. Using one of these standards, a training author may build predefined paths through the training content, possibly influenced by predefined conditions. Thus, rather than a static path created at the start of training, these standards enable a dynamic path based on an individual student-user's progression through the training.
For example, suppose that the training author wished to teach five concepts, with each concept being taught using either an example-based or theory-based learning strategy. The training author would again create ten sections, one example-based and one theory-based for each of the five concepts. The training author could again also optionally create, for example, an overview section for all student-users. However, rather than the student-user selecting a learning strategy, the training author could manually predefine paths based on certain conditions, for example test results. Thus, for example, the training author could specify that if a student-user scored 60% or higher on the content mastery test after completing the example-based section for Content 1, then the example-based section for Content 2 should be used. On the other hand, the training author could also specify, for example, that if a student-user scored under 60% on the content mastery test after completing the example-based section for Content 1, then the theory-based section for Content 2 should be used. Thus, the path of sections through which that user would progress would depend on his or her performance on each content mastery test.
Although these standards allow dynamic generation of a path through the training material, they still present several drawbacks. First, the training author using such a standard must manually define the paths when creating the course. For example, if the training author wishes to use content mastery test results as a condition for determining the next learning strategy section, then the author must explicitly account for all possible content mastery test results and define how each result affects which learning strategy to use for the next concept. Second, the student-user may not explicitly change the learning strategy. For example, if a student-user knows he or she learns best by example but if a given example-based section is unclear, the student-user may score poorly on the content mastery test for that section and be wrongly directed to a theory-based section for the next concept. Third, the system may not automatically switch among learning strategies during runtime. For example, if the training author defines inappropriate or suboptimal conditions affecting the path, the system may not change the author's definitions in order to create a more appropriate learning path for the student-user. Fourth, the system does not use information from other trainings to compute a suitable path. Thus, for example, the system may not apply knowledge about the student-user's preferred learning strategy or strategies acquired during previous trainings completed by that student-user.
Thus, the need exists for an enhanced CBT system that dynamically provides adaptive learning strategies for each individual student-user.